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TRV-2026-1009Certified recordPeer-reviewed

Improving 10-year cardiovascular disease risk prediction using automated machine learning

Aims To develop a cardiovascular disease (CVD) risk prediction model with improved accuracy and interpretability by integrating diverse risk factors and applying Automated Machine Learning (AutoML), thereby enhancing clinical utility over conventional models. Methods This is a prospective cohort study. Data were obtained from the Multi-Ethnic Study of Atherosclerosis (MESA), including baseline and fifth follow-up visits, comprising 4713 participants. Exercise and dietary data were harmonized via Metabolic Equiva…

Health · G Space — documented gain · certified 2026-09-07 · v1 · article view · machine-readable

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H2O AutoML trained on 4713 MESA participants using 21 selected predictors achieved higher discrimination than logistic regression and traditional ML models, reaching AUC 0.882 and accuracy 0.864, intended as a practical tool for clinician risk stratification.

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Truvace Impact Record TRV-2026-1009, v1: “Improving 10-year cardiovascular disease risk prediction using automated machine learning.” Truvace, 2026-09-07. /record/TRV-2026-1009 (accessed at citation time). sha256 430789624b46e10b…

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